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Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.

The objective of the conference is to bring together academic researchers, educators, doctoral students and practitioners from various international institutions to focus on timely financial issues and research findings pertaining to industrialized and developing countries including the recent financial and economic crisis.

Behavioural Finance Working Group(BFWG), Queen Mary, University of London, UK and the Society for the Study of Emerging Markets, USA will hold a two day meeting on 14-15 June 2018. In this two-day meeting which is jointly organized by the Behavioural Finance Working Group (BFWG), Queen Mary, University of London, UK, and the Society for the Study of Emerging Markets, USA, we will consider how the fields of behavioural finance, economic psychology, financial socio-analysis and other related areas can enhance our understanding of financial behaviours. Papers exploring any Behavioural Finance issue will be considered, but those related to the influence of sentiment and mood on the decision-making of individuals (e.g. consumption, saving, borrow, investing) and/or corporations (e.g. financing, investing, pay-out policy), and the implications of such for markets and economic policy, will be particularly welcomed.

Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which numerous client services are offered. In particular, Financial Organisations are creating and leveraging such innovation in the domain of wealth management. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants.